Working analysis

Survey questions

Q1. Before receiving this survey, did you know influenza is different from the stomach flu?

# Q1 summary
with(data2, table(Q1))
## Q1
##   NA   No  Yes 
##   16  488 1664
q1 <- data2 %>%
  count(Q1)

# plot with this one
ggplot(data2[!is.na(data2$Q1), ]) + geom_bar(mapping = aes(x = Q1, fill = Q1))

# ggplot(q1, aes(x = Q1, y = n, fill = Q1)) + geom_bar(stat = 'identity')

# plot without na's
#ggplot(q1[!is.na(q1$Q1), ], aes(x = Q1, y = n, fill = Q1)) +
#  geom_bar(stat = 'identity', position = position_dodge())



# by gender, PPGENDER
with(data2, table(PPGENDER, Q1))
##         Q1
## PPGENDER  NA  No Yes
##   Female   4 205 888
##   Male    12 283 776
q1 <- data2 %>%
  count(Q1, PPGENDER)

# plot
ggplot(data2[!is.na(data2$Q1), ]) + geom_bar(mapping = aes(x = Q1, fill = PPGENDER), position = position_dodge())

# ggplot(q1[!is.na(q1$Q1), ], aes(x = Q1, y = n, fill = PPGENDER)) +
#   geom_bar(stat = 'identity', position = position_dodge())

# plot with facet
ggplot(q1[!is.na(q1$Q1), ], aes(x = Q1, y = n, fill = Q1)) +
  geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~PPGENDER)

# by ethnicity, PPETHM
with(data2, table(PPETHM, Q1))
##                         Q1
## PPETHM                     NA   No  Yes
##   2+ Races, Non-Hispanic    0   18   62
##   Black, Non-Hispanic       2   50  143
##   Hispanic                  2   69  161
##   Other, Non-Hispanic       1   29   63
##   White, Non-Hispanic      11  322 1235
q1 <- data2 %>%
  count(Q1, PPETHM)

# plot
ggplot(q1[!is.na(q1$Q1), ], aes(x = Q1, y = n, fill = PPETHM)) +
  geom_bar(stat = 'identity', position = position_dodge())

# plot with facet
ggplot(q1[!is.na(q1$Q1), ], aes(x = Q1, y = n, fill = Q1)) +
  geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~PPETHM)

# by income, PPINCIMP
with(data2, table(PPINCIMP, Q1))
##                       Q1
## PPINCIMP                NA  No Yes
##   Less than $5,000       1  22  30
##   $5,000 to $7,499       1   8  16
##   $7,500 to $9,999       0   7   7
##   $10,000 to $12,499     0  17  39
##   $12,500 to $14,999     0  10  38
##   $15,000 to $19,999     1  22  40
##   $20,000 to $24,999     2  16  55
##   $25,000 to $29,999     0  23  76
##   $30,000 to $34,999     2  21  70
##   $35,000 to $39,999     1  31  72
##   $40,000 to $49,999     0  42 107
##   $50,000 to $59,999     1  46 137
##   $60,000 to $74,999     2  50 172
##   $75,000 to $84,999     1  26 133
##   $85,000 to $99,999     0  33 120
##   $100,000 to $124,999   2  56 269
##   $125,000 to $149,999   0  24 108
##   $150,000 to $174,999   1  16  68
##   $175,000 or more       1  18 107
q1 <- data2 %>%
  count(Q1, PPINCIMP)

# plot
ggplot(q1[!is.na(q1$Q1), ], aes(x = Q1, y = n, fill = PPINCIMP)) +
  geom_bar(stat = 'identity', position = position_dodge())

# plot with facet
ggplot(q1[!is.na(q1$Q1), ], aes(x = Q1, y = n, fill = Q1)) +
  geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~PPINCIMP)

Q2. Have you had an illness with influenza-like symptoms since August 2015?

#
with(data2, table(Q2))
## Q2
##   NA   No  Yes 
##   19 1735  414
q2 <- data2 %>%
  count(Q2)
ggplot(q2, aes(x = Q2, y = n, fill = Q2)) + geom_bar(stat = 'identity')

# by gender
with(data2, table(Q2, PPGENDER))
##      PPGENDER
## Q2    Female Male
##   NA       5   14
##   No     858  877
##   Yes    234  180
q2 <- data2 %>%
  count(Q2, PPGENDER)
ggplot(q2, aes(x = Q2, y = n, fill = PPGENDER)) +
  geom_bar(stat = 'identity', position = position_dodge())

# by ethnicity
with(data2, table(Q2, PPETHM))
##      PPETHM
## Q2    2+ Races, Non-Hispanic Black, Non-Hispanic Hispanic
##   NA                       0                   4        3
##   No                      61                 152      164
##   Yes                     19                  39       65
##      PPETHM
## Q2    Other, Non-Hispanic White, Non-Hispanic
##   NA                    0                  12
##   No                   71                1287
##   Yes                  22                 269
q2 <- data2 %>%
  count(Q2, PPETHM)
ggplot(q2, aes(x = Q2, y = n, fill = PPETHM)) +
  geom_bar(stat = 'identity', position = position_dodge())

# by income
with(data2, table(Q2, PPINCIMP))
##      PPINCIMP
## Q2    Less than $5,000 $5,000 to $7,499 $7,500 to $9,999
##   NA                 1                0                0
##   No                43               19               13
##   Yes                9                6                1
##      PPINCIMP
## Q2    $10,000 to $12,499 $12,500 to $14,999 $15,000 to $19,999
##   NA                   1                  0                  2
##   No                  38                 39                 46
##   Yes                 17                  9                 15
##      PPINCIMP
## Q2    $20,000 to $24,999 $25,000 to $29,999 $30,000 to $34,999
##   NA                   1                  1                  1
##   No                  55                 79                 74
##   Yes                 17                 19                 18
##      PPINCIMP
## Q2    $35,000 to $39,999 $40,000 to $49,999 $50,000 to $59,999
##   NA                   1                  1                  2
##   No                  85                121                155
##   Yes                 18                 27                 27
##      PPINCIMP
## Q2    $60,000 to $74,999 $75,000 to $84,999 $85,000 to $99,999
##   NA                   2                  1                  1
##   No                 172                130                123
##   Yes                 50                 29                 29
##      PPINCIMP
## Q2    $100,000 to $124,999 $125,000 to $149,999 $150,000 to $174,999
##   NA                     1                    0                    2
##   No                   265                  112                   62
##   Yes                   61                   20                   21
##      PPINCIMP
## Q2    $175,000 or more
##   NA                 1
##   No               104
##   Yes               21
q2 <- data2 %>%
  count(Q2, PPINCIMP)
ggplot(q2, aes(x = Q2, y = n, fill = PPINCIMP)) +
  geom_bar(stat = 'identity', position = position_dodge())

Q3. Has any other person in your household had an illness with influenza like symptoms since August 2015?

# all
with(data2, table(Q3))
## Q3
## Don_t know         NA         No        Yes 
##        161         16       1608        383
q3 <- data2 %>%
  count(Q3)
ggplot(q3, aes(x = Q3, y = n, fill = Q3)) + geom_bar(stat = 'identity')

# by gender
with(data2, table(Q3, PPGENDER))
##             PPGENDER
## Q3           Female Male
##   Don_t know     72   89
##   NA              4   12
##   No            804  804
##   Yes           217  166
q3 <- data2 %>%
  count(Q3, PPGENDER)
ggplot(q3, aes(x = Q3, y = n, fill = PPGENDER)) +
  geom_bar(stat = 'identity', position = position_dodge())

# by ethnicity
with(data2, table(Q3, PPETHM))
##             PPETHM
## Q3           2+ Races, Non-Hispanic Black, Non-Hispanic Hispanic
##   Don_t know                      6                  19       30
##   NA                              0                   2        3
##   No                             57                 149      146
##   Yes                            17                  25       53
##             PPETHM
## Q3           Other, Non-Hispanic White, Non-Hispanic
##   Don_t know                  11                  95
##   NA                           0                  11
##   No                          59                1197
##   Yes                         23                 265
q3 <- data2 %>%
  count(Q3, PPETHM)
ggplot(q3, aes(x = Q3, y = n, fill = PPETHM)) +
  geom_bar(stat = 'identity', position = position_dodge())

# by income
with(data2, table(Q3, PPINCIMP))
##             PPINCIMP
## Q3           Less than $5,000 $5,000 to $7,499 $7,500 to $9,999
##   Don_t know               11                6                1
##   NA                        1                0                0
##   No                       36               18               13
##   Yes                       5                1                0
##             PPINCIMP
## Q3           $10,000 to $12,499 $12,500 to $14,999 $15,000 to $19,999
##   Don_t know                  4                  7                  7
##   NA                          0                  0                  1
##   No                         44                 30                 47
##   Yes                         8                 11                  8
##             PPINCIMP
## Q3           $20,000 to $24,999 $25,000 to $29,999 $30,000 to $34,999
##   Don_t know                  8                  4                 11
##   NA                          1                  1                  3
##   No                         52                 81                 70
##   Yes                        12                 13                  9
##             PPINCIMP
## Q3           $35,000 to $39,999 $40,000 to $49,999 $50,000 to $59,999
##   Don_t know                 11                  6                 13
##   NA                          1                  1                  2
##   No                         75                117                136
##   Yes                        17                 25                 33
##             PPINCIMP
## Q3           $60,000 to $74,999 $75,000 to $84,999 $85,000 to $99,999
##   Don_t know                 18                  7                 11
##   NA                          2                  0                  0
##   No                        165                120                107
##   Yes                        39                 33                 35
##             PPINCIMP
## Q3           $100,000 to $124,999 $125,000 to $149,999
##   Don_t know                   20                    6
##   NA                            1                    0
##   No                          245                  100
##   Yes                          61                   26
##             PPINCIMP
## Q3           $150,000 to $174,999 $175,000 or more
##   Don_t know                    3                7
##   NA                            1                1
##   No                           58               94
##   Yes                          23               24
q3 <- data2 %>%
  count(Q3, PPINCIMP)
ggplot(q3, aes(x = Q3, y = n, fill = PPINCIMP)) +
  geom_bar(stat = 'identity', position = position_dodge())

Q4. Does your job require you to have a lot of contact with the public?

# all
with(data2, table(Q4))
## Q4
##                                                       NA 
##                                                       18 
##                                         No, I don_t work 
##                                                      779 
## No, my job does not require much contact with the public 
##                                                      620 
##                                                      Yes 
##                                                      751
(
q4 <- data2 %>%
  count(Q4)
)
## Source: local data frame [4 x 2]
## 
##                                                         Q4     n
##                                                     <fctr> <int>
## 1                                                       NA    18
## 2                                         No, I don_t work   779
## 3 No, my job does not require much contact with the public   620
## 4                                                      Yes   751
ggplot(q4, aes(x = Q4, y = n, fill = Q4)) + geom_bar(stat = 'identity') +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))

# by gender
with(data2, table(Q4, PPGENDER))
##                                                           PPGENDER
## Q4                                                         Female Male
##   NA                                                            4   14
##   No, I don_t work                                            430  349
##   No, my job does not require much contact with the public    263  357
##   Yes                                                         400  351
q4 <- data2 %>%
  count(Q4, PPGENDER)
ggplot(q4, aes(x = Q4, y = n, fill = PPGENDER)) +
  geom_bar(stat = 'identity', position = position_dodge()) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# by ethnicity
with(data2, table(Q4, PPETHM))
##                                                           PPETHM
## Q4                                                         2+ Races, Non-Hispanic
##   NA                                                                            0
##   No, I don_t work                                                             30
##   No, my job does not require much contact with the public                     23
##   Yes                                                                          27
##                                                           PPETHM
## Q4                                                         Black, Non-Hispanic
##   NA                                                                         3
##   No, I don_t work                                                          69
##   No, my job does not require much contact with the public                  59
##   Yes                                                                       64
##                                                           PPETHM
## Q4                                                         Hispanic
##   NA                                                              4
##   No, I don_t work                                               69
##   No, my job does not require much contact with the public       72
##   Yes                                                            87
##                                                           PPETHM
## Q4                                                         Other, Non-Hispanic
##   NA                                                                         0
##   No, I don_t work                                                          24
##   No, my job does not require much contact with the public                  34
##   Yes                                                                       35
##                                                           PPETHM
## Q4                                                         White, Non-Hispanic
##   NA                                                                        11
##   No, I don_t work                                                         587
##   No, my job does not require much contact with the public                 432
##   Yes                                                                      538
q4 <- data2 %>%
  count(Q4, PPETHM)
ggplot(q4, aes(x = Q4, y = n, fill = PPETHM)) +
  geom_bar(stat = 'identity', position = position_dodge()) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# by income 
with(data2, table(Q4, PPINCIMP))
##                                                           PPINCIMP
## Q4                                                         Less than $5,000
##   NA                                                                      1
##   No, I don_t work                                                       29
##   No, my job does not require much contact with the public               17
##   Yes                                                                     6
##                                                           PPINCIMP
## Q4                                                         $5,000 to $7,499
##   NA                                                                      0
##   No, I don_t work                                                       15
##   No, my job does not require much contact with the public                5
##   Yes                                                                     5
##                                                           PPINCIMP
## Q4                                                         $7,500 to $9,999
##   NA                                                                      0
##   No, I don_t work                                                       11
##   No, my job does not require much contact with the public                1
##   Yes                                                                     2
##                                                           PPINCIMP
## Q4                                                         $10,000 to $12,499
##   NA                                                                        1
##   No, I don_t work                                                         33
##   No, my job does not require much contact with the public                  7
##   Yes                                                                      15
##                                                           PPINCIMP
## Q4                                                         $12,500 to $14,999
##   NA                                                                        0
##   No, I don_t work                                                         32
##   No, my job does not require much contact with the public                  5
##   Yes                                                                      11
##                                                           PPINCIMP
## Q4                                                         $15,000 to $19,999
##   NA                                                                        1
##   No, I don_t work                                                         28
##   No, my job does not require much contact with the public                 13
##   Yes                                                                      21
##                                                           PPINCIMP
## Q4                                                         $20,000 to $24,999
##   NA                                                                        1
##   No, I don_t work                                                         35
##   No, my job does not require much contact with the public                 18
##   Yes                                                                      19
##                                                           PPINCIMP
## Q4                                                         $25,000 to $29,999
##   NA                                                                        1
##   No, I don_t work                                                         46
##   No, my job does not require much contact with the public                 15
##   Yes                                                                      37
##                                                           PPINCIMP
## Q4                                                         $30,000 to $34,999
##   NA                                                                        1
##   No, I don_t work                                                         38
##   No, my job does not require much contact with the public                 25
##   Yes                                                                      29
##                                                           PPINCIMP
## Q4                                                         $35,000 to $39,999
##   NA                                                                        1
##   No, I don_t work                                                         42
##   No, my job does not require much contact with the public                 22
##   Yes                                                                      39
##                                                           PPINCIMP
## Q4                                                         $40,000 to $49,999
##   NA                                                                        1
##   No, I don_t work                                                         64
##   No, my job does not require much contact with the public                 41
##   Yes                                                                      43
##                                                           PPINCIMP
## Q4                                                         $50,000 to $59,999
##   NA                                                                        3
##   No, I don_t work                                                         60
##   No, my job does not require much contact with the public                 58
##   Yes                                                                      63
##                                                           PPINCIMP
## Q4                                                         $60,000 to $74,999
##   NA                                                                        3
##   No, I don_t work                                                         73
##   No, my job does not require much contact with the public                 60
##   Yes                                                                      88
##                                                           PPINCIMP
## Q4                                                         $75,000 to $84,999
##   NA                                                                        0
##   No, I don_t work                                                         45
##   No, my job does not require much contact with the public                 51
##   Yes                                                                      64
##                                                           PPINCIMP
## Q4                                                         $85,000 to $99,999
##   NA                                                                        0
##   No, I don_t work                                                         47
##   No, my job does not require much contact with the public                 48
##   Yes                                                                      58
##                                                           PPINCIMP
## Q4                                                         $100,000 to $124,999
##   NA                                                                          2
##   No, I don_t work                                                           87
##   No, my job does not require much contact with the public                  111
##   Yes                                                                       127
##                                                           PPINCIMP
## Q4                                                         $125,000 to $149,999
##   NA                                                                          0
##   No, I don_t work                                                           39
##   No, my job does not require much contact with the public                   51
##   Yes                                                                        42
##                                                           PPINCIMP
## Q4                                                         $150,000 to $174,999
##   NA                                                                          1
##   No, I don_t work                                                           23
##   No, my job does not require much contact with the public                   25
##   Yes                                                                        36
##                                                           PPINCIMP
## Q4                                                         $175,000 or more
##   NA                                                                      1
##   No, I don_t work                                                       32
##   No, my job does not require much contact with the public               47
##   Yes                                                                    46
q4 <- data2 %>%
  count(Q4, PPINCIMP)
ggplot(q4, aes(x = Q4, y = n, fill = PPINCIMP)) +
  geom_bar(stat = 'identity', position = position_dodge()) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Q5. Do you have a car that you can use to travel to work?

# all
with(data2, table(Q5))
## Q5
##   NA   No  Yes 
##  800  133 1235
q5 <- data2 %>%
  count(Q5)
ggplot(q5, aes(x = Q5, y = n, fill = Q5)) + geom_bar(stat = 'identity')

# by gender
with(data2, table(PPGENDER, Q5))
##         Q5
## PPGENDER  NA  No Yes
##   Female 435  70 592
##   Male   365  63 643
q5 <- data2 %>%
  count(Q5, PPGENDER)
ggplot(q5, aes(x = Q5, y = n, fill = PPGENDER)) +
  geom_bar(stat = 'identity', position = position_dodge())

# by ethnicity 
q5 <- data2 %>%
  count(Q5, PPETHM)
ggplot(q5, aes(x = Q5, y = n, fill = PPETHM)) +
  geom_bar(stat = 'identity', position = position_dodge())

# by income 
q5 <- data2 %>%
  count(Q5, PPINCIMP)
ggplot(q5, aes(x = Q5, y = n, fill = PPINCIMP)) +
  geom_bar(stat = 'identity', position = position_dodge())

Q6. Do you regularly use public transportation?

# all
with(data2, table(Q6))
## Q6
##   NA   No  Yes 
##   15 1959  194
q6 <- data2 %>%
  count(Q6)
ggplot(q6, aes(x = Q6, y = n, fill = Q6)) + geom_bar(stat = 'identity')

# by gender
# with(data2, table(PPGENDER, Q6))
(q6 <- data2 %>%
  count(Q6, PPGENDER)
)
## Source: local data frame [6 x 3]
## Groups: Q6 [?]
## 
##       Q6 PPGENDER     n
##   <fctr>   <fctr> <int>
## 1     NA   Female     3
## 2     NA     Male    12
## 3     No   Female   998
## 4     No     Male   961
## 5    Yes   Female    96
## 6    Yes     Male    98
ggplot(q6, aes(x = Q6, y = n, fill = PPGENDER)) +
  geom_bar(stat = 'identity', position = position_dodge())

# by ethnicity 
(q6 <- data2 %>%
  count(Q6, PPETHM)
)
## Source: local data frame [13 x 3]
## Groups: Q6 [?]
## 
##        Q6                 PPETHM     n
##    <fctr>                 <fctr> <int>
## 1      NA    Black, Non-Hispanic     1
## 2      NA               Hispanic     4
## 3      NA    White, Non-Hispanic    10
## 4      No 2+ Races, Non-Hispanic    62
## 5      No    Black, Non-Hispanic   158
## 6      No               Hispanic   196
## 7      No    Other, Non-Hispanic    80
## 8      No    White, Non-Hispanic  1463
## 9     Yes 2+ Races, Non-Hispanic    18
## 10    Yes    Black, Non-Hispanic    36
## 11    Yes               Hispanic    32
## 12    Yes    Other, Non-Hispanic    13
## 13    Yes    White, Non-Hispanic    95
ggplot(q6, aes(x = Q6, y = n, fill = PPETHM)) +
  geom_bar(stat = 'identity', position = position_dodge())

# by income 
(q6 <- data2 %>%
  count(Q6, PPINCIMP)
)
## Source: local data frame [50 x 3]
## Groups: Q6 [?]
## 
##        Q6             PPINCIMP     n
##    <fctr>               <fctr> <int>
## 1      NA     Less than $5,000     1
## 2      NA   $12,500 to $14,999     1
## 3      NA   $15,000 to $19,999     1
## 4      NA   $20,000 to $24,999     1
## 5      NA   $25,000 to $29,999     1
## 6      NA   $30,000 to $34,999     1
## 7      NA   $40,000 to $49,999     1
## 8      NA   $50,000 to $59,999     1
## 9      NA   $60,000 to $74,999     4
## 10     NA $100,000 to $124,999     1
## ..    ...                  ...   ...
ggplot(q6, aes(x = Q6, y = n, fill = PPINCIMP)) +
  geom_bar(stat = 'identity', position = position_dodge())

Q7. What types of public transportation do you regularly use?

# look at patterned names
# grep("Q7", names(data2))

# make long data
Q7 <- data2 %>%
  gather("Q7_q", "Q7_r", starts_with("Q7_"), -contains("Text"), -contains("Refused"), na.rm = TRUE)

#grep("Q7", names(Q7))
#View(Q7[c(1, 34, 35, 423:424)])


with(Q7, table(Q7_q, Q7_r))
##                Q7_r
## Q7_q              NA   No  Yes
##   Q7_1_Bus      1974   57  137
##   Q7_2_Carpool  1974  184   10
##   Q7_3_Subway   1974  131   63
##   Q7_4_Train    1974  139   55
##   Q7_5_Taxi     1974  169   25
##   Q7_6_Airplane 1974  175   19
##   Q7_7_Other    1974  179   15
q7 <- Q7 %>%
  count(Q7_q, Q7_r)

# flip coordinates
ggplot(q7[!is.na(q7$Q7_r), ], aes(x = Q7_r, y = n, fill = Q7_r)) +
  geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~Q7_q) + coord_flip()

# by gender
# with(Q7, table(PPGENDER, r, q))
(q7 <- Q7 %>%
  group_by(PPGENDER, Q7_q, Q7_r) %>%
  count(PPGENDER, Q7_q, Q7_r)
)
## Source: local data frame [42 x 4]
## Groups: PPGENDER, Q7_q [?]
## 
##    PPGENDER         Q7_q  Q7_r     n
##      <fctr>        <chr> <chr> <int>
## 1    Female     Q7_1_Bus    NA  1001
## 2    Female     Q7_1_Bus    No    27
## 3    Female     Q7_1_Bus   Yes    69
## 4    Female Q7_2_Carpool    NA  1001
## 5    Female Q7_2_Carpool    No    91
## 6    Female Q7_2_Carpool   Yes     5
## 7    Female  Q7_3_Subway    NA  1001
## 8    Female  Q7_3_Subway    No    68
## 9    Female  Q7_3_Subway   Yes    28
## 10   Female   Q7_4_Train    NA  1001
## ..      ...          ...   ...   ...
ggplot(q7[!is.na(q7$Q7_r), ], aes(x = Q7_r, y = n, fill = PPGENDER)) +
  geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~Q7_q)

# by ethnicity
# with(Q7, table(PPETHM, r, q))
(q7 <- Q7 %>%
  group_by(PPETHM, Q7_q, Q7_r) %>%
  count(PPETHM, Q7_q, Q7_r)
)
## Source: local data frame [100 x 4]
## Groups: PPETHM, Q7_q [?]
## 
##                    PPETHM         Q7_q  Q7_r     n
##                    <fctr>        <chr> <chr> <int>
## 1  2+ Races, Non-Hispanic     Q7_1_Bus    NA    62
## 2  2+ Races, Non-Hispanic     Q7_1_Bus    No     4
## 3  2+ Races, Non-Hispanic     Q7_1_Bus   Yes    14
## 4  2+ Races, Non-Hispanic Q7_2_Carpool    NA    62
## 5  2+ Races, Non-Hispanic Q7_2_Carpool    No    18
## 6  2+ Races, Non-Hispanic  Q7_3_Subway    NA    62
## 7  2+ Races, Non-Hispanic  Q7_3_Subway    No    12
## 8  2+ Races, Non-Hispanic  Q7_3_Subway   Yes     6
## 9  2+ Races, Non-Hispanic   Q7_4_Train    NA    62
## 10 2+ Races, Non-Hispanic   Q7_4_Train    No    15
## ..                    ...          ...   ...   ...
ggplot(q7[!is.na(q7$Q7_r), ], aes(x = Q7_r, y = n, fill = PPETHM)) +
  geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~Q7_q)

# by income
# with(Q7, table(q, r, PPINCIMP))
(q7 <- Q7 %>%
  group_by(PPINCIMP, Q7_q, Q7_r) %>%
  count(PPINCIMP, Q7_q, Q7_r)
)
## Source: local data frame [357 x 4]
## Groups: PPINCIMP, Q7_q [?]
## 
##            PPINCIMP         Q7_q  Q7_r     n
##              <fctr>        <chr> <chr> <int>
## 1  Less than $5,000     Q7_1_Bus    NA    43
## 2  Less than $5,000     Q7_1_Bus   Yes    10
## 3  Less than $5,000 Q7_2_Carpool    NA    43
## 4  Less than $5,000 Q7_2_Carpool    No    10
## 5  Less than $5,000  Q7_3_Subway    NA    43
## 6  Less than $5,000  Q7_3_Subway    No     9
## 7  Less than $5,000  Q7_3_Subway   Yes     1
## 8  Less than $5,000   Q7_4_Train    NA    43
## 9  Less than $5,000   Q7_4_Train    No     8
## 10 Less than $5,000   Q7_4_Train   Yes     2
## ..              ...          ...   ...   ...
ggplot(q7[!is.na(q7$Q7_r), ], aes(x = Q7_r, y = n, fill = PPINCIMP)) +
  geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~Q7_q)

Q8. For what types of activities do you regularly use public transportation?

Q8 <- data2 %>%
  gather("Q8_q", "Q8_r", starts_with("Q8_"), -contains("otherText"), -contains("Refused"))

with(Q8, table(Q8_q, Q8_r))
##                       Q8_r
## Q8_q                     NA   No  Yes
##   Q8_1_Work            1974   89  105
##   Q8_2_School          1974  158   36
##   Q8_3_Shopping        1974  107   87
##   Q8_4_Visiting.people 1974  125   69
##   Q8_5_Recreation      1974  127   67
##   Q8_6_Other           1974  175   19
q8 <- Q8 %>%
  count(Q8_q, Q8_r)

Q9. Do other members of your household regularly use public transportation?

with(data2, table(Q9))
## Q9
## Don_t know         NA         No        Yes 
##         32         18       1935        183

Q10. What types of public transportation do other members of your household regularly use?

#Q10 <- data2 %>%
#  select(CaseID, PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, #Q10_1_Bus:Q10_9_Refused) %>%
#  gather("Q10_q", "Q10_r", Q10_1_Bus:Q10_8_Other)


Q10 <- data2 %>%
  gather("Q10_q", "Q10_r", starts_with("Q10_"), -contains("Text"), -contains("Refused"), na.rm = TRUE)


with(Q10, table(Q10_q, Q10_r))
##                   Q10_r
## Q10_q                NA   No  Yes
##   Q10_1_Bus        1985   48  135
##   Q10_2_Carpool    1985  166   17
##   Q10_3_Subway     1985  130   53
##   Q10_4_Train      1985  137   46
##   Q10_5_Taxi       1985  157   26
##   Q10_6_Airplane   1985  164   19
##   Q10_7_Don_t.know 1985  182    1
##   Q10_8_Other      1985  172   11
q10 <- Q10 %>%
  count(Q10_q, Q10_r)

Q11. How do you rate your risk of getting influenza if you visited each of the following locations?

Q11 <- data2 %>%
  select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, Q11_1_Work:Q11_OtherText_Codes) %>%
  gather("q", "r", Q11_1_Work:Q11_11_Other)


# all
with(Q11, table(q, r))
##                              r
## q                             Don_t Know High Risk, Very Likely
##   Q11_1_Work                         185                    524
##   Q11_10_Family.or.friends           121                    541
##   Q11_11_Other                       915                     51
##   Q11_2_Schools                      178                    909
##   Q11_3_Day.care                     214                    924
##   Q11_4_Stores                       115                    551
##   Q11_5_Restaurants                  111                    483
##   Q11_6_Libraries                    169                    386
##   Q11_7_Hospitals                    123                    982
##   Q11_8_Doctor_s.office              110                    994
##   Q11_9_Public.transportation        147                   1093
##                              r
## q                             Low Risk, Not Likely
##   Q11_1_Work                                   643
##   Q11_10_Family.or.friends                     485
##   Q11_11_Other                                 104
##   Q11_2_Schools                                508
##   Q11_3_Day.care                               554
##   Q11_4_Stores                                 405
##   Q11_5_Restaurants                            442
##   Q11_6_Libraries                              700
##   Q11_7_Hospitals                              374
##   Q11_8_Doctor_s.office                        308
##   Q11_9_Public.transportation                  353
##                              r
## q                             Medium Risk, Somewhat Likely
##   Q11_1_Work                                           795
##   Q11_10_Family.or.friends                            1000
##   Q11_11_Other                                          54
##   Q11_2_Schools                                        551
##   Q11_3_Day.care                                       454
##   Q11_4_Stores                                        1076
##   Q11_5_Restaurants                                   1111
##   Q11_6_Libraries                                      890
##   Q11_7_Hospitals                                      669
##   Q11_8_Doctor_s.office                                733
##   Q11_9_Public.transportation                          551
q11 <- Q11 %>%
  count(q, r)
ggplot(q11[!is.na(q11$r), ], aes(x = r, y = n, fill = r)) +
  geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~q) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# by gender
# with(Q7, table(PPGENDER, r, q))
(q11 <- Q11 %>%
  group_by(PPGENDER, q, r) %>%
  count(PPGENDER, q, r)
)
## Source: local data frame [110 x 4]
## Groups: PPGENDER, q [?]
## 
##    PPGENDER                        q                            r     n
##      <fctr>                    <chr>                        <chr> <int>
## 1    Female               Q11_1_Work                   Don_t Know    89
## 2    Female               Q11_1_Work       High Risk, Very Likely   309
## 3    Female               Q11_1_Work         Low Risk, Not Likely   310
## 4    Female               Q11_1_Work Medium Risk, Somewhat Likely   381
## 5    Female               Q11_1_Work                           NA     8
## 6    Female Q11_10_Family.or.friends                   Don_t Know    53
## 7    Female Q11_10_Family.or.friends       High Risk, Very Likely   302
## 8    Female Q11_10_Family.or.friends         Low Risk, Not Likely   229
## 9    Female Q11_10_Family.or.friends Medium Risk, Somewhat Likely   506
## 10   Female Q11_10_Family.or.friends                           NA     7
## ..      ...                      ...                          ...   ...
ggplot(q11[!is.na(q11$r), ], aes(x = r, y = n, fill = PPGENDER)) +
  geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~q) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# by ethnicity
# with(Q7, table(PPETHM, r, q))
(q11 <- Q11 %>%
  group_by(PPETHM, q, r) %>%
  count(PPETHM, q, r)
)
## Source: local data frame [275 x 4]
## Groups: PPETHM, q [?]
## 
##                    PPETHM                        q
##                    <fctr>                    <chr>
## 1  2+ Races, Non-Hispanic               Q11_1_Work
## 2  2+ Races, Non-Hispanic               Q11_1_Work
## 3  2+ Races, Non-Hispanic               Q11_1_Work
## 4  2+ Races, Non-Hispanic               Q11_1_Work
## 5  2+ Races, Non-Hispanic               Q11_1_Work
## 6  2+ Races, Non-Hispanic Q11_10_Family.or.friends
## 7  2+ Races, Non-Hispanic Q11_10_Family.or.friends
## 8  2+ Races, Non-Hispanic Q11_10_Family.or.friends
## 9  2+ Races, Non-Hispanic Q11_10_Family.or.friends
## 10 2+ Races, Non-Hispanic Q11_10_Family.or.friends
## ..                    ...                      ...
## Variables not shown: r <chr>, n <int>.
ggplot(q11[!is.na(q11$r), ], aes(x = r, y = n, fill = PPETHM)) +
  geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~q) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# by income
# with(Q7, table(q, r, PPINCIMP))
(q11 <- Q11 %>%
  group_by(PPINCIMP, q, r) %>%
  count(PPINCIMP, q, r)
)
## Source: local data frame [985 x 4]
## Groups: PPINCIMP, q [?]
## 
##            PPINCIMP                        q                            r
##              <fctr>                    <chr>                        <chr>
## 1  Less than $5,000               Q11_1_Work                   Don_t Know
## 2  Less than $5,000               Q11_1_Work       High Risk, Very Likely
## 3  Less than $5,000               Q11_1_Work         Low Risk, Not Likely
## 4  Less than $5,000               Q11_1_Work Medium Risk, Somewhat Likely
## 5  Less than $5,000               Q11_1_Work                           NA
## 6  Less than $5,000 Q11_10_Family.or.friends                   Don_t Know
## 7  Less than $5,000 Q11_10_Family.or.friends       High Risk, Very Likely
## 8  Less than $5,000 Q11_10_Family.or.friends         Low Risk, Not Likely
## 9  Less than $5,000 Q11_10_Family.or.friends Medium Risk, Somewhat Likely
## 10 Less than $5,000 Q11_10_Family.or.friends                           NA
## ..              ...                      ...                          ...
## Variables not shown: n <int>.
ggplot(q11[!is.na(q11$r), ], aes(x = r, y = n, fill = PPINCIMP)) +
  geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~q) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Q12. Which of the following actions do you take to avoid getting sick?

Q12 <- data2 %>%
  select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 75:91) %>%
  gather("q", "r", 7:21)

with(Q12, table(q, r))
##                                                      r
## q                                                     Always   NA Never
##   Q12_1_Avoid.touching.my.eyes                           653   23   324
##   Q12_10_Get.recommended.vaccine                        1041   23   564
##   Q12_11_Take.preventive.medicine                        425   22   831
##   Q12_12_Cover.my.nose.and.mouth.with.a.surgical.mask    218   24  1568
##   Q12_13_Avoid.contact.with.people.who.are.sick          765   22   153
##   Q12_14_Avoid.crowded.places                            406   27   413
##   Q12_15_Other                                            91 1518   472
##   Q12_2_Avoid.touching.my.nose                           613   23   349
##   Q12_3_Avoid.touching.my.mouth                          758   25   300
##   Q12_4_Wash.my.hands.with.soap.more.often              1774   25    52
##   Q12_5_Use.hand.sanitizers                              911   22   278
##   Q12_6_Clean.the.surfaces.in.my.home                   1132   22   115
##   Q12_7_Clean.the.surfaces.at.work                       752   30   544
##   Q12_8_Eat.nutritious.food                              895   22   107
##   Q12_9_Get.adequate.rest                                899   25   114
##                                                      r
## q                                                     Sometimes
##   Q12_1_Avoid.touching.my.eyes                             1168
##   Q12_10_Get.recommended.vaccine                            540
##   Q12_11_Take.preventive.medicine                           890
##   Q12_12_Cover.my.nose.and.mouth.with.a.surgical.mask       358
##   Q12_13_Avoid.contact.with.people.who.are.sick            1228
##   Q12_14_Avoid.crowded.places                              1322
##   Q12_15_Other                                               87
##   Q12_2_Avoid.touching.my.nose                             1183
##   Q12_3_Avoid.touching.my.mouth                            1085
##   Q12_4_Wash.my.hands.with.soap.more.often                  317
##   Q12_5_Use.hand.sanitizers                                 957
##   Q12_6_Clean.the.surfaces.in.my.home                       899
##   Q12_7_Clean.the.surfaces.at.work                          842
##   Q12_8_Eat.nutritious.food                                1144
##   Q12_9_Get.adequate.rest                                  1130
q12 <- Q12 %>%
  count(q, r)

Q13. Do you get the flu vaccine?

with(data2, table(Q13))
## Q13
##              NA       No, never Yes, every year Yes, some years 
##              18             819             908             423
ggplot(data2[!is.na(data2$Q13), ]) + geom_bar(mapping = aes(x = Q13, fill = Q13), position = position_dodge())

Q14. How much do you pay to get an influenza vaccine?

with(data2, table(Q14))
## Q14
##            $0    $30 to $60    Don_t know Less than $30 More than $60 
##           970            54            80           222             4 
##            NA 
##           838
ggplot(data2[!is.na(data2$Q14), ]) + geom_bar(mapping = aes(x = Q14, fill = Q14), position = position_dodge())

# by gender
with(data2, by(Q14, PPGENDER, summary))
## PPGENDER: Female
##            $0    $30 to $60    Don_t know Less than $30 More than $60 
##           514            28            41           101             2 
##            NA 
##           411 
## -------------------------------------------------------- 
## PPGENDER: Male
##            $0    $30 to $60    Don_t know Less than $30 More than $60 
##           456            26            39           121             2 
##            NA 
##           427

Q15. Are you more likely to get a vaccine if others around you get a vaccine?

with(data2, table(Q15))
## Q15
##               NA  No, less likely    No, no effect Yes, more likely 
##              839               70              878              381
ggplot(data2[!is.na(data2$Q15), ]) + geom_bar(mapping = aes(x = Q15, fill = Q15), position = position_dodge())

Q16. Are you more likely to get a vaccine if others around you do not get a vaccine?

with(data2, table(Q16))
## Q16
##               NA  No, less likely    No, no effect Yes, more likely 
##              850              101              904              313
ggplot(data2[!is.na(data2$Q16), ]) + geom_bar(mapping = aes(x = Q16, fill = Q16), position = position_dodge())

Q17. Do you get a vaccine to protect yourself, protect others, or protect yourself and others?

with(data2, table(Q17))
## Q17
##                        NA            Protect myself 
##                       844                       381 
## Protect myself and others            Protect others 
##                       921                        22
ggplot(data2[!is.na(data2$Q17), ]) + geom_bar(mapping = aes(x = Q17, fill = Q17), position = position_dodge())

Q18. What are the reasons you would not get an influenza vaccine?

Q18 <- data2 %>%
  select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 97:108) %>%
  gather("q", "r", 7:Q18_10_Other)

with(Q18, table(q, r))
##                                                                  r
## q                                                                   NA
##   Q18_1_The.vaccine.costs.too.much                                 926
##   Q18_10_Other                                                     926
##   Q18_2_The.vaccine.is.not.very.effective.in.preventing.influenza  926
##   Q18_3_I.am.not.likely.to.get.influenza                           926
##   Q18_4_Do.not.know.where.to.get.vaccine                           926
##   Q18_5_The.side.effect.of.the.vaccine.are.too.risky               926
##   Q18_6_I.am.allergic.to.some.of.the.ingredients.in.the.vaccine    926
##   Q18_7_I.do.not.like.shots                                        926
##   Q18_8_I.just.don_t.get.around.to.doing.it                        926
##   Q18_9_I.have.to.travel.too.far.to.get.vaccine                    926
##                                                                  r
## q                                                                   No
##   Q18_1_The.vaccine.costs.too.much                                1132
##   Q18_10_Other                                                    1064
##   Q18_2_The.vaccine.is.not.very.effective.in.preventing.influenza  903
##   Q18_3_I.am.not.likely.to.get.influenza                           964
##   Q18_4_Do.not.know.where.to.get.vaccine                          1199
##   Q18_5_The.side.effect.of.the.vaccine.are.too.risky               958
##   Q18_6_I.am.allergic.to.some.of.the.ingredients.in.the.vaccine   1184
##   Q18_7_I.do.not.like.shots                                        976
##   Q18_8_I.just.don_t.get.around.to.doing.it                        878
##   Q18_9_I.have.to.travel.too.far.to.get.vaccine                   1216
##                                                                  r
## q                                                                  Yes
##   Q18_1_The.vaccine.costs.too.much                                 110
##   Q18_10_Other                                                     178
##   Q18_2_The.vaccine.is.not.very.effective.in.preventing.influenza  339
##   Q18_3_I.am.not.likely.to.get.influenza                           278
##   Q18_4_Do.not.know.where.to.get.vaccine                            43
##   Q18_5_The.side.effect.of.the.vaccine.are.too.risky               284
##   Q18_6_I.am.allergic.to.some.of.the.ingredients.in.the.vaccine     58
##   Q18_7_I.do.not.like.shots                                        266
##   Q18_8_I.just.don_t.get.around.to.doing.it                        364
##   Q18_9_I.have.to.travel.too.far.to.get.vaccine                     26
q18 <- Q18 %>%
  count(q, r)

Q19. Do you have health insurance?

with(data2, table(Q19))
## Q19
##   NA   No  Yes 
##   20  154 1994
ggplot(data2[!is.na(data2$Q19), ]) + geom_bar(mapping = aes(x = Q19, fill = Q19), position = position_dodge())

Q20. How effective do you think the influenza vaccine is in protecting people from becoming sick with influenza?

with(data2, table(Q20))
## Q20
##                      Don_t know It varies from season to season 
##                             228                             433 
##                              NA                   Not effective 
##                              19                             144 
##              Somewhat effective                  Very effective 
##                             961                             383
ggplot(data2[!is.na(data2$Q20), ]) + geom_bar(mapping = aes(x = Q20, fill = Q20), position = position_dodge())

Q21. Are influenza vaccines covered by your health insurance?

with(data2, table(Q21))
## Q21
##                             Don_t know 
##                                    500 
##                                     NA 
##                                    178 
##                                     No 
##                                     55 
## Yes, but only part of the cost is paid 
##                                    153 
##             Yes, the full cost is paid 
##                                   1282
ggplot(data2[!is.na(data2$Q21), ]) + geom_bar(mapping = aes(x = Q21, fill = Q21), position = position_dodge())

Q22. Do you do any of the following when you have influenza symptoms?

Q22 <- data2 %>%
  select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 112:122) %>%
  gather("q", "r", 7:Q22_9_Other)

with(Q22, table(q, r))
##                                                                     r
## q                                                                    Always
##   Q22_1_Go.to.a.doctor_s.office.or.medical.clinic                       349
##   Q22_2_Decide.on.treatment.without.consulting.a.health.practitioner    335
##   Q22_3_Search.the.internet.for.a.treatment                             126
##   Q22_4_Get.adequate.sleep                                             1147
##   Q22_5_Eat.nutritious.food                                             909
##   Q22_6_Take.over.counter.medication.for.symptoms                       796
##   Q22_7_Take.an.antiviral.medicine                                      153
##   Q22_8_Take.no.action.to.treat.the.illness                              96
##   Q22_9_Other                                                            54
##                                                                     r
## q                                                                      NA
##   Q22_1_Go.to.a.doctor_s.office.or.medical.clinic                      32
##   Q22_2_Decide.on.treatment.without.consulting.a.health.practitioner   31
##   Q22_3_Search.the.internet.for.a.treatment                            33
##   Q22_4_Get.adequate.sleep                                             31
##   Q22_5_Eat.nutritious.food                                            33
##   Q22_6_Take.over.counter.medication.for.symptoms                      32
##   Q22_7_Take.an.antiviral.medicine                                     35
##   Q22_8_Take.no.action.to.treat.the.illness                            34
##   Q22_9_Other                                                        1628
##                                                                     r
## q                                                                    Never
##   Q22_1_Go.to.a.doctor_s.office.or.medical.clinic                      552
##   Q22_2_Decide.on.treatment.without.consulting.a.health.practitioner   473
##   Q22_3_Search.the.internet.for.a.treatment                           1148
##   Q22_4_Get.adequate.sleep                                             115
##   Q22_5_Eat.nutritious.food                                            135
##   Q22_6_Take.over.counter.medication.for.symptoms                      210
##   Q22_7_Take.an.antiviral.medicine                                    1103
##   Q22_8_Take.no.action.to.treat.the.illness                           1199
##   Q22_9_Other                                                          448
##                                                                     r
## q                                                                    Sometimes
##   Q22_1_Go.to.a.doctor_s.office.or.medical.clinic                         1235
##   Q22_2_Decide.on.treatment.without.consulting.a.health.practitioner      1329
##   Q22_3_Search.the.internet.for.a.treatment                                861
##   Q22_4_Get.adequate.sleep                                                 875
##   Q22_5_Eat.nutritious.food                                               1091
##   Q22_6_Take.over.counter.medication.for.symptoms                         1130
##   Q22_7_Take.an.antiviral.medicine                                         877
##   Q22_8_Take.no.action.to.treat.the.illness                                839
##   Q22_9_Other                                                               38
q22 <- Q22 %>%
  count(q, r)

Q23. Which of the following actions do you take when you have influenza symptoms to avoid someone else from getting sick?

Q23 <- data2 %>%
  select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 123:Q23_11_Other) %>%
  gather("q", "r", 7:Q23_11_Other)

with(Q23, table(q, r))
##                                                        r
## q                                                       Always   NA Never
##   Q23_1_Stand.away.from.people                            1006   31   135
##   Q23_10_Cover.my.nose.and.mouth.when.I.sneeze.or.cough   1717   29    81
##   Q23_11_Other                                              54 1665   421
##   Q23_2_Avoid.public.places                                897   31   196
##   Q23_3_Avoid.public.transportation                       1342   31   245
##   Q23_4_Stay.at.home                                       869   30   163
##   Q23_5_Wash.my.hands.with.soap.more.often                1559   29    92
##   Q23_6_Use.hand.sanitizers                               1014   30   299
##   Q23_7_Clean.the.surfaces.in.my.home                     1151   32   153
##   Q23_8_Clean.the.surfaces.I.use.at.work                   856   32   508
##   Q23_9_Cover.my.nose.and.mouth.with.a.surgical.mask       267   29  1463
##                                                        r
## q                                                       Sometimes
##   Q23_1_Stand.away.from.people                                996
##   Q23_10_Cover.my.nose.and.mouth.when.I.sneeze.or.cough       341
##   Q23_11_Other                                                 28
##   Q23_2_Avoid.public.places                                  1044
##   Q23_3_Avoid.public.transportation                           550
##   Q23_4_Stay.at.home                                         1106
##   Q23_5_Wash.my.hands.with.soap.more.often                    488
##   Q23_6_Use.hand.sanitizers                                   825
##   Q23_7_Clean.the.surfaces.in.my.home                         832
##   Q23_8_Clean.the.surfaces.I.use.at.work                      772
##   Q23_9_Cover.my.nose.and.mouth.with.a.surgical.mask          409
q23 <- Q23 %>%
  count(q, r)

Q24. What sources of information do you recall hearing or seeing about influenza outbreaks?

Q24 <- data2 %>%
  select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 137:Q24_7_Refused) %>%
  gather("q", "r", 7:Q24_6_Other)

with(Q24, table(q, r))
##                                                       r
## q                                                        No  Yes
##   Q24_1_Print.media.such.as.newspapers.and.magazines   1460  708
##   Q24_2_Traditional.media.such.as.television.and.radio  811 1357
##   Q24_3_Social.media.such.as.internet.and.blogs        1680  488
##   Q24_4_Word.of.mouth                                  1213  955
##   Q24_5_None                                           1764  404
##   Q24_6_Other                                          2114   54
q24 <- Q24 %>%
  count(q, r)

Q25. If you received information from the news, internet or other public media that there was an influenza outbreak in your community would you do any of the following?

Q25 <- data2 %>%
  select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 145:Q25_11_Other) %>%
  gather("q", "r", 7:Q25_11_Other)

with(Q25, table(q, r))
##                                                        r
## q                                                       Always   NA Never
##   Q25_1_Stand.away.from.people                             649   34   217
##   Q25_10_Cover.my.nose.and.mouth.when.I.sneeze.or.cough   1643   36    90
##   Q25_11_Other                                              32 1722   393
##   Q25_2_Avoid.public.places                                648   33   270
##   Q25_3_Avoid.public.transportation                       1221   36   268
##   Q25_4_Stay.at.home                                       484   33   429
##   Q25_5_Wash.my.hands.with.soap.more.often                1477   38    99
##   Q25_6_Use.hand.sanitizers                               1077   35   257
##   Q25_7_Clean.the.surfaces.in.my.home                     1116   35   160
##   Q25_8_Clean.the.surfaces.I.use.at.work                   902   36   464
##   Q25_9_Cover.my.nose.and.mouth.with.a.surgical.mask       343   34  1286
##                                                        r
## q                                                       Sometimes
##   Q25_1_Stand.away.from.people                               1268
##   Q25_10_Cover.my.nose.and.mouth.when.I.sneeze.or.cough       399
##   Q25_11_Other                                                 21
##   Q25_2_Avoid.public.places                                  1217
##   Q25_3_Avoid.public.transportation                           643
##   Q25_4_Stay.at.home                                         1222
##   Q25_5_Wash.my.hands.with.soap.more.often                    554
##   Q25_6_Use.hand.sanitizers                                   799
##   Q25_7_Clean.the.surfaces.in.my.home                         857
##   Q25_8_Clean.the.surfaces.I.use.at.work                      766
##   Q25_9_Cover.my.nose.and.mouth.with.a.surgical.mask          505
q25 <- Q25 %>%
  count(q, r)

Q26. Does your household have children?

with(data2, table(Q26))
## Q26
##   NA   No  Yes 
##   22 1570  576
ggplot(data2[!is.na(data2$Q26), ]) + geom_bar(mapping = aes(x = Q26, fill = Q26), position = position_dodge())

Q27. What actions do you take when a child in your household has influenza symptoms?

Q27 <- data2 %>%
  select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 159:Q27_4_Other) %>%
  gather("q", "r", 7:Q27_4_Other)

with(Q27, table(q, r))
##                                                             r
## q                                                            Always   NA
##   Q27_1_Keep.the.child.away.from.the.others.in.the.residence    198 1595
##   Q27_2_Keep.the.child.out.of.school.daycare                    377 1596
##   Q27_3_Stop.child_s.social.activities.like.play.dates          388 1595
##   Q27_4_Other                                                    12 2051
##                                                             r
## q                                                            Never
##   Q27_1_Keep.the.child.away.from.the.others.in.the.residence    90
##   Q27_2_Keep.the.child.out.of.school.daycare                    46
##   Q27_3_Stop.child_s.social.activities.like.play.dates          41
##   Q27_4_Other                                                   93
##                                                             r
## q                                                            Sometimes
##   Q27_1_Keep.the.child.away.from.the.others.in.the.residence       285
##   Q27_2_Keep.the.child.out.of.school.daycare                       149
##   Q27_3_Stop.child_s.social.activities.like.play.dates             144
##   Q27_4_Other                                                       12
q27 <- Q27 %>%
  count(q, r)

Q28. Are you a single parent?

with(data2, table(Q28))
## Q28
##   NA   No  Yes 
## 1592  490   86
ggplot(data2[!is.na(data2$Q28), ]) + geom_bar(mapping = aes(x = Q28, fill = Q28), position = position_dodge())

Q29. How do you care for a sick child?

Q29 <- data2 %>%
  select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 166:Q29_6_Other) %>%
  gather("q", "r", 7:Q29_6_Other)

with(Q29, table(q, r))
##                                                r
## q                                               Always   NA Never
##   Q29_1_A.parent.brings.the.child.to.work            7 1682   438
##   Q29_2_A.parent.stays.home                        266 1682    27
##   Q29_3_Another.adult.stays.home                    68 1682   202
##   Q29_4_Send.the.child.to.school.sick                1 1683   414
##   Q29_5_Take.the.child.to.a.relative.or.friends      8 1682   292
##   Q29_6_Other                                        4 2082    76
##                                                r
## q                                               Sometimes
##   Q29_1_A.parent.brings.the.child.to.work              41
##   Q29_2_A.parent.stays.home                           193
##   Q29_3_Another.adult.stays.home                      216
##   Q29_4_Send.the.child.to.school.sick                  70
##   Q29_5_Take.the.child.to.a.relative.or.friends       186
##   Q29_6_Other                                           6
q29 <- Q29 %>%
  count(q, r)

Q30. How do you care for a sick child?

Q30 <- data2 %>%
  select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 174:Q30_6_Other) %>%
  gather("q", "r", 7:Q30_6_Other)

with(Q30, table(q, r))
##                                                r
## q                                               Always   NA Never
##   Q30_1_I.bring.the.child.to.work                    4 2082    77
##   Q30_2_I.stay.home                                 34 2082    10
##   Q30_3_Another.adult.stays.home                     9 2082    25
##   Q30_4_Send.the.child.to.school.sick                3 2082    60
##   Q30_5_Take.the.child.to.a.relative.or.friends      7 2082    33
##   Q30_6_Other                                        1 2150    14
##                                                r
## q                                               Sometimes
##   Q30_1_I.bring.the.child.to.work                       5
##   Q30_2_I.stay.home                                    42
##   Q30_3_Another.adult.stays.home                       52
##   Q30_4_Send.the.child.to.school.sick                  23
##   Q30_5_Take.the.child.to.a.relative.or.friends        46
##   Q30_6_Other                                           3
q30 <- Q30 %>%
  count(q, r)

Q31. How many hours of screen time (time spent watching television, a computer, smartphone, iPad, etc.) do you spend each day on average when you are not sick? Enter 0 if none

with(data2, summary(Q31))
##   0   1  10  11  12  13  14  15  16  17  18   2  20  21  22  24   3   4 
##  72 161 137  13  55   1  12  20   8   3   5 336   9   1   1   2 328 329 
##   5   6   7   8   9  NA 
## 224 167  48 141  43  52
# by gender
with(data2, by(Q31, PPGENDER, summary))
## PPGENDER: Female
##   0   1  10  11  12  13  14  15  16  17  18   2  20  21  22  24   3   4 
##  33  77  72   6  33   0   8  10   5   1   1 184   0   1   0   0 172 154 
##   5   6   7   8   9  NA 
## 109  86  23  82  19  21 
## -------------------------------------------------------- 
## PPGENDER: Male
##   0   1  10  11  12  13  14  15  16  17  18   2  20  21  22  24   3   4 
##  39  84  65   7  22   1   4  10   3   2   4 152   9   0   1   2 156 175 
##   5   6   7   8   9  NA 
## 115  81  25  59  24  31

Q32. How many hours of screen time do you spend each day on average when you are sick? Enter 0 if none

with(data2, summary(Q32))
##   0   1  10  11  12  14  15  16  17  18  19   2  20  21  22  24   3   4 
## 365 204  94   2  67   6  15   3   1   5   1 256  11   1   1   3 208 209 
##   5   6   7   8   9  NA 
## 185 217  44 186  23  61
# by gender
with(data2, by(Q33, PPGENDER, summary))
## PPGENDER: Female
##   1  11  13  14   2   3   4   5   6   7   8   9  NA 
## 220   0   0   0 433 190 148  62  22   9   3   2   8 
## -------------------------------------------------------- 
## PPGENDER: Male
##   1  11  13  14   2   3   4   5   6   7   8   9  NA 
## 215   1   1   1 424 166 141  69  17  13   2   1  20

Q33. How many people, including yourself, reside in your household?

with(data2, summary(Q33))
##   1  11  13  14   2   3   4   5   6   7   8   9  NA 
## 435   1   1   1 857 356 289 131  39  22   5   3  28
# by ethnicity
with(data2, by(Q33, PPETHM, summary))
## PPETHM: 2+ Races, Non-Hispanic
##  1 11 13 14  2  3  4  5  6  7  8  9 NA 
## 14  0  0  0 31 15  9  6  1  3  0  0  1 
## -------------------------------------------------------- 
## PPETHM: Black, Non-Hispanic
##  1 11 13 14  2  3  4  5  6  7  8  9 NA 
## 53  0  1  0 56 44 25  8  3  1  2  0  2 
## -------------------------------------------------------- 
## PPETHM: Hispanic
##  1 11 13 14  2  3  4  5  6  7  8  9 NA 
## 50  0  0  0 60 38 42 24  4  6  0  2  6 
## -------------------------------------------------------- 
## PPETHM: Other, Non-Hispanic
##  1 11 13 14  2  3  4  5  6  7  8  9 NA 
## 14  0  0  0 30 14 21  8  4  1  0  0  1 
## -------------------------------------------------------- 
## PPETHM: White, Non-Hispanic
##   1  11  13  14   2   3   4   5   6   7   8   9  NA 
## 304   1   0   1 680 245 192  85  27  11   3   1  18

Household Members

HHM1

Q35. What is the gender of this member of the household? Remember, this relates to HHM1_Name who is HHM1_AGE years old.

with(data2, table(Q35))
## Q35
## Female   Male     NA 
##    799    859    510

Q36. On average, how many days per week does this member of your household work or attend day care or school outside of your home?

with(data2, summary(Q36))
##   0   1   2   3   4   5   6   7  NA 
## 638  31  30  34  85 661  75  43 571

Q37. On average, how many days per week does this member of your household participate in social activities outside of your home?

with(data2, summary(Q37))
##   0   1   2   3   4   5   6   7  NA 
## 425 245 326 167 104 142  39  57 663

Q38. On average, how many days per week does this member of your household use public transportation?

with(data2, summary(Q38))
##    0    1    2    3    4    5    6    7   NA 
## 1435   16   22   13   12   66   11   11  582

Q39. How frequently does this member of your household visit a doctor’s office for wellness appointments?

with(data2, summary(Q39))
##              Don_t know Less than once per year More than once per year 
##                      84                     222                     593 
##                      NA                   Never           Once per year 
##                     513                     100                     656

Q40. How frequently does this member of the household get sick in a typical year?

with(data2, summary(Q40))
##  1 to 2 times  3 to 5 times 6 to 10 times    Don_t know  More than 10 
##          1025           271            32            87            13 
##            NA         Never 
##           514           226

Q41. How many times has this member of your household had influenza or another respiratory illness in the last two years?

with(data2, summary(Q41))
##     2 times     3 times  Don_t know More than 3          NA       Never 
##         191          60         158          39         513         807 
##        Once 
##         400

Q42. Does this member of your household get an annual influenza vaccine?

with(data2, summary(Q42))
##     Don_t know             NA      No, never    Yes, always Yes, sometimes 
##            166            511            567            661            263

HHM2

Q43. What is the gender of this member of the household? Remember, this relates to HHM1_Name who is HHM1_AGE years old.

with(data2, summary(Q43))
## Female   Male     NA 
##    388    431   1349

Q44. On average, how many days per week does this member of your household work or attend day care or school outside of your home?

with(data2, summary(Q44))
##    0    1    2    3    4    5    6    7   NA 
##  192   10   20   22   28  449   41   23 1383

Q45. On average, how many days per week does this member of your household participate in social activities outside of your home?

with(data2, summary(Q45))
##    0    1    2    3    4    5    6    7   NA 
##  166  117  152  116   62   74   27   35 1419

Q46. On average, how many days per week does this member of your household use public transportation?

with(data2, summary(Q46))
##    0    1    2    3    4    5    6    7   NA 
##  668   12   11    9    6   67    3    1 1391

Q47. How frequently does this member of your household visit a doctor’s office for wellness appointments?

with(data2, summary(Q47))
##              Don_t know Less than once per year More than once per year 
##                      55                     125                     232 
##                      NA                   Never           Once per year 
##                    1344                      59                     353

Q48. How frequently does this member of the household get sick in a typical year?

with(data2, summary(Q48))
##  1 to 2 times  3 to 5 times 6 to 10 times    Don_t know  More than 10 
##           490           153            25            66             7 
##            NA         Never 
##          1344            83

Q49. How many times has this member of your household had influenza or another respiratory illness in the last two years?

with(data2, summary(Q49))
##     2 times     3 times  Don_t know More than 3          NA       Never 
##          91          32          93          21        1345         403 
##        Once 
##         183

Q50. Does this member of your household get an annual influenza vaccine?

with(data2, summary(Q50))
##     Don_t know             NA      No, never    Yes, always Yes, sometimes 
##            100           1344            317            275            132